Paper Citation Record · LEDGER

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.20480.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.20480 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:44:53.156757Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7fd42812-9f5a-40bd-a569-73ef1ee24bae · outbound

This paper cites Distribution grid impedance & topology estimation with limited or no micro-pmus,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Distribution grid impedance & topology estimation with limited or no micro-pmus,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.400645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b84d4743-764f-4c28-bc8c-cb3b87ae3f9d · outbound

This paper cites Hd-deep-em: Deep expectation maximization for dynamic hidden state recovery using heterogeneous data,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Hd-deep-em: Deep expectation maximization for dynamic hidden state recovery using heterogeneous data,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.484456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 256752a2-1330-4059-a1fa-c8ee25a5f0a7 · outbound

This paper cites Distributed algorithms for convexified bad data and topology error detection and identification problems,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Distributed algorithms for convexified bad data and topology error detection and identification problems,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.577100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1148d80b-13a2-4976-ab94-8345cc237b5e · outbound

This paper cites Machine learning-enabled distribution network phase identification,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Machine learning-enabled distribution network phase identification,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.726018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 677092be-1034-4f86-8103-8fa06f51f8d5 · outbound

This paper cites Guaranteed con- version from static measurements into dynamic ones based on manifold feature interpolation,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Guaranteed con- version from static measurements into dynamic ones based on manifold feature interpolation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.798806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73fc12a4-9aea-4a26-954d-abdc360a5949 · outbound

This paper cites Tajer, S.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Tajer, S

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.907958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aa0c90a-e449-4174-b728-39c7841d726f · outbound

This paper cites Efficient manifold-constrained neural ode for high-dimensional datasets,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Efficient manifold-constrained neural ode for high-dimensional datasets,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:51.995900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91713f5c-a7ac-4303-9c33-eedc5885a999 · outbound

This paper cites Graph mining for classifying and localizing solar panels in distribution grids,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Graph mining for classifying and localizing solar panels in distribution grids,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.098757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12339069-e8a2-4b52-ab9d-d3db771f71dc · outbound

This paper cites Identifying errors in service transformer connections,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Identifying errors in service transformer connections,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.172848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33084421-f688-4e7e-ab08-735228ae5d3c · outbound

This paper cites Core process representation in power system operational models: Gaps, challenges, and opportunities for multisector dynamics research,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Core process representation in power system operational models: Gaps, challenges, and opportunities for multisector dynamics research,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.240677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4afdb540-bdff-4961-9ddc-9ea884eaf9b4 · outbound

This paper cites Data quality challenges in existing distribution network datasets,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Data quality challenges in existing distribution network datasets,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.351345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f78dd6b2-a892-46a9-aa35-4d3b0b84416c · outbound

This paper cites An efficient approach to power system uncertainty analysis with high-dimensional dependencies,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference An efficient approach to power system uncertainty analysis with high-dimensional dependencies,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.409518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 100baadd-368c-493a-9c88-a9416173a783 · outbound

This paper cites Spatial-temporal deep learning for hosting capacity analysis in distribution grids,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Spatial-temporal deep learning for hosting capacity analysis in distribution grids,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.480166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0f7b257-fbb5-48d6-b694-c8d34206c7ec · outbound

This paper cites Solar photovoltaic assessment with large lan- guage model,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Solar photovoltaic assessment with large lan- guage model,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.553509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d88878b5-335f-4929-94ee-6c7ce6fb25ab · outbound

This paper cites Topology identification and line parameter estimation for non-pmu distribution network: A numerical method,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Topology identification and line parameter estimation for non-pmu distribution network: A numerical method,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.659482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0b988be-f85c-47ec-b1d4-97562f49740d · outbound

This paper cites ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.733395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9e181e6-3ad7-4b44-b783-77381e658e56 · outbound

This paper cites Phase identification in electric power distribution systems by clustering of smart meter data,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Phase identification in electric power distribution systems by clustering of smart meter data,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.799330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 570ccfa4-e7ae-4c56-967f-ec56fc769321 · outbound

This paper cites An introduction to optimal power flow: Theory, formulation, and examples,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference An introduction to optimal power flow: Theory, formulation, and examples,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.904367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59716f27-385f-48e8-9244-ca9592825ac5 · outbound

This paper cites Physical equation discovery using physics- consistent neural network (pcnn) under incomplete observability,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Physical equation discovery using physics- consistent neural network (pcnn) under incomplete observability,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:52.979476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f12d3f18-0e38-4c44-880a-6f0e23656ba1 · outbound

This paper cites Adaptive data fusion for state estimation and control of power grids under attack,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference Adaptive data fusion for state estimation and control of power grids under attack,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:53.050730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f1f69d5-43d2-4e84-b448-b348ae851f80 · outbound

This paper cites A joint estimation method of distribution network topology and line parameters based on power flow graph convolutional networks,.

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference A joint estimation method of distribution network topology and line parameters based on power flow graph convolutional networks,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T12:44:53.156757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.